A Survey on the Progression and Performance of Generative Adversarial Networks

Ghosh, Bhaskar; Dutta, Indira Kalyan; Totaro, Michael W.; Bayoumi, Magdy

doi:10.1109/icccnt49239.2020.9225510

Cited by 16 publications

(13 citation statements)

References 34 publications

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“…regularization methods, or lass functions) and review how the researchers deal with that problem. And, in the third category, a plethora of survey studies [7], [9], [19], [31]- [43] summarize the application of GAN in a specific field, from computer vision and image synthesis, to cybersecurity and anomaly detection. In the following, we briefly review surveys in each category and express how our paper differs from the others.…”

Section: Related Surveysmentioning

confidence: 99%

“…Creswell et al [11] provide an overview of GANs, especially for the signal processing community, by characterizing different methods for training and constructing GANs, and challenges in the theory and applications. In [19] Ghosh et al present a comprehensive summary on the progression and performance of GANs along with their various applications. Saxena et al in [20] conduct a survey of the advancements in GANs design and optimization solutions proposed to handle GANs challenges.…”

Section: A Gan General Surveysmentioning

confidence: 99%

“…As another application of GANs, [39] and [19] structure reviews on the GANs in the cybersecurity. Yinka et al [39] survey studies where the GAN plays a key role in the design of a security system or adversarial system.…”

Section: Gan Applicationsmentioning

confidence: 99%

“…Yinka et al [39] survey studies where the GAN plays a key role in the design of a security system or adversarial system. Ghosh et al [19] focus on the various ways in which GANs have been used to provide both security advances and attack scenarios in order to bypass detection systems.…”

Section: Gan Applicationsmentioning

confidence: 99%

See 3 more Smart Citations

Game of GANs: Game-Theoretical Models for Generative Adversarial Networks

Moghadam¹,

Boroomand²,

Barzegar-Jalali³

et al. 2021

Preprint

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Generative Adversarial Network, as a promising research direction in the AI community, recently attracts considerable attention due to its ability to generating high-quality realistic data. GANs are a competing game between two neural networks trained in an adversarial manner to reach a Nash equilibrium. Despite the improvement accomplished in GANs in the last years, there remain several issues to solve. In this way, how to tackle these issues and make advances leads to rising research interests. This paper reviews literature that leverages the game theory in GANs and addresses how game models can relieve specific generative models' challenges and improve the GAN's performance. In particular, we firstly review some preliminaries, including the basic GAN model and some game theory backgrounds. After that, we present our taxonomy to summarize the state-of-the-art solutions into three significant categories: modified game model, modified architecture, and modified learning method. The classification is based on the modifications made in the basic model by the proposed approaches from the game-theoretic perspective. We further classify each category into several subcategories. Following the proposed taxonomy, we explore the main objective of each class and review the recent work in each group. Finally, we discuss the remaining challenges in this field and present the potential future research topics.

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Section: Related Surveysmentioning

confidence: 99%

Section: A Gan General Surveysmentioning

confidence: 99%

Section: Gan Applicationsmentioning

confidence: 99%

Section: Gan Applicationsmentioning

confidence: 99%

See 2 more Smart Citations

Game of GANs: Game-Theoretical Models for Generative Adversarial Networks

Moghadam¹,

Boroomand²,

Barzegar-Jalali³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…GAN surveys are usually focused on GAN models structure [163,43] or their application in certain tasks [157,4]. Because we will focus on novel GAN architectures, this survey can be identified as the first type.…”

Section: Introductionmentioning

confidence: 99%

A survey on GANs for computer vision: Recent research, analysis and taxonomy

Iglesias¹,

Talavera²,

Díaz-Álvarez³

2022

Preprint

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In the last few years, there have been several revolutions in the field of deep learning, mainly headlined by the large impact of Generative Adversarial Networks (GANs). GANs not only provide an unique architecture when defining their models, but also generate incredible results which have had a direct impact on society. Due to the significant improvements and new areas of research that GANs have brought, the community is constantly coming up with new researches that make it almost impossible to keep up with the times. Our survey aims to provide a general overview of GANs, showing the latest architectures, optimizations of the loss functions, validation metrics and application areas of the most widely recognized variants. The efficiency of the different variants of the model architecture will be evaluated, as well as showing the best application area; as a vital part of the process, the different metrics for evaluating the performance of GANs and the frequently used loss functions will be analyzed.The final objective of this survey is to provide a summary of the evolution and performance of the GANs which are having better results to guide future researchers in the field.

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Obj-SA-GAN: Object-Driven Text-to-Image Synthesis with Self-Attention Based Full Semantic Information Mining

Yang

et al. 2022

Lecture Notes in Computer Science

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A Survey on the Progression and Performance of Generative Adversarial Networks

Cited by 16 publications

References 34 publications

Game of GANs: Game-Theoretical Models for Generative Adversarial Networks

Game of GANs: Game-Theoretical Models for Generative Adversarial Networks

A survey on GANs for computer vision: Recent research, analysis and taxonomy

Obj-SA-GAN: Object-Driven Text-to-Image Synthesis with Self-Attention Based Full Semantic Information Mining

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